Paper: Learning Semantic-Level Information Extraction Rules By Type-Oriented ILP

ACL ID C00-2101
Title Learning Semantic-Level Information Extraction Rules By Type-Oriented ILP
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2000
Authors

This paper describes an approach to using se- mantic rcprcsentations for learning information extraction (IE) rules by a type-oriented induc- tire logic programming (ILl)) system. NLP components of a lnachine translation system are used to automatically generate semantic repre- sentations of text corpus that can be given di- rectly to an ILP system. The latest experimen- tal results show high precision and recall of the learned rules.